.2 procent
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@ -3,7 +3,7 @@ import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.pyplot as plt
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import seaborn as sns
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import seaborn as sns
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import csv
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import csv
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, MaxAbsScaler
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from enum import Enum
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from enum import Enum
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import random
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import random
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from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, matthews_corrcoef
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from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, matthews_corrcoef
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@ -19,7 +19,7 @@ class Tree(Enum):
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PLATAAN = 7
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PLATAAN = 7
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# Open file
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# Open file
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file = open('./out/result-2023-10-10T15.08.36.csv', "r")
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file = open('dataset\\csv\\result-2023-10-14T16.13.30.csv', "r")
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data = list(csv.reader(file, delimiter=","))
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data = list(csv.reader(file, delimiter=","))
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file.close()
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file.close()
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@ -32,6 +32,7 @@ tags_int = []
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for row in data:
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for row in data:
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tree = row.pop(0)
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tree = row.pop(0)
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row.pop(1) # TODO: Doe dit niet
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id = Tree[tree.upper()]
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id = Tree[tree.upper()]
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# print("Tree name =", tree, " id =", id.value)
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# print("Tree name =", tree, " id =", id.value)
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@ -52,14 +53,16 @@ for idx, col in enumerate(data[0]):
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column = np.array(column).reshape(-1, 1)
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column = np.array(column).reshape(-1, 1)
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# Perform Min - Max scaling
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# Perform Min - Max scaling
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scaler = MinMaxScaler()
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# scaler = MinMaxScaler()
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scaler = MaxAbsScaler()
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column = scaler.fit_transform(column)
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column = scaler.fit_transform(column)
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# Reshape it back cus scaler is dumb af
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# Reshape it back cus scaler is dumb af
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column = np.array(column).reshape(len(column))
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column = np.array(column).reshape(len(column))
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# DEBUG Print resulting column
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# DEBUG Print resulting column
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# print("NORM", header[idx + 1], "\n", column)
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print("NORM", header[idx + 1], "\n", column)
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# Replace original data array
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# Replace original data array
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data[:, idx] = column
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data[:, idx] = column
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